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Document worth reading: “Towards learning-to-learn”

In good old-fashioned artificial intelligence (GOFAI), humans specified systems that solved problems. Much of the recent progress in AI has come from replacing human insights by learning. However, learning itself…

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Document worth reading: “Query Expansion Techniques for Information Retrieval: a Survey”

With the ever increasing size of web, relevant information extraction on the Internet with a query formed by a few keywords has become a big challenge. To overcome this, query…

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Document worth reading: “Generative Adversarial Networks: A Survey and Taxonomy”

Generative adversarial networks (GANs) have been extensively studied in the past few years. Arguably the revolutionary techniques are in the area of computer vision such as plausible image generation, image…

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Document worth reading: “High Dimensional Classification via Empirical Risk Minimization: Improvements and Optimality”

In this article, we investigate a family of classification algorithms defined by the principle of empirical risk minimization, in the high dimensional regime where the feature dimension $p$ and data…

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Document worth reading: “Machine Learning Methods Economists Should Know About”

We discuss the relevance of the recent Machine Learning (ML) literature for economics and econometrics. First we discuss the differences in goals, methods and settings between the ML literature and…

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Document worth reading: “A Survey on Graph Kernels”

Graph kernels have become an established and widely-used technique for solving classification tasks on graphs. This survey gives a comprehensive overview of techniques for kernel-based graph classification developed in the…

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Document worth reading: “An Overview of Open-Ended Evolution: Editorial Introduction to the Open-Ended Evolution II Special Issue”

Nature’s spectacular inventiveness, reflected in the enormous diversity of form and function displayed by the biosphere, is a feature of life that distinguishes living most strongly from nonliving. It is,…

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Document worth reading: “A Survey on Neural Architecture Search”

The growing interest in both the automation of machine learning and deep learning has inevitably led to the development of automated methods for neural architecture optimization. The choice of the…

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Document worth reading: “An Introduction to Variational Autoencoders”

Variational autoencoders provide a principled framework for learning deep latent-variable models and corresponding inference models. In this work, we provide an introduction to variational autoencoders and some important extensions. An…

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Document worth reading: “A Review of Changepoint Detection Models”

The objective of the change-point detection is to discover the abrupt property changes lying behind the time-series data. In this paper, we firstly summarize the definition and in-depth implication of…